Sutton present a strategic research plan based on the premise that a genuine understanding of intelligence is imminent and—when it is achieved—will be the greatest scientific prize in human history. To contribute to this achievement and share in its glory will require laser-like focus on its essential challenges; identifying those, however provisionally, is the objective of the Alberta Plan for AI research. The overall setting is the familiar one common to many fields (reinforcement learning, psychology, control theory, economics, neuroscience, and operations research): a computationally-limited agent interacts with a vastly more complex environment to maximize reward. The agent’s machinery is divided into four parts: 1) that which maintains the agent’s situational state (perception), 2) that which maps state to action (policy), 3) that which maps state to expected future reward (value function), and 4) that which maps imagined states and actions to next states (transition model) and enables planning. The Alberta Plan extends this common view to include feature-based subtasks and temporally extended options to solve them; the policy and the value function each become multiple, one each for each of the subtasks and the main task. The setting is then potentially complete and the focus shifts to finding the right abstractions, in state (features) and time (options), and to planning efficiency. The Alberta Plan incorporates continual learning and meta-learning into all of its 12 steps, and expends no effort trying to capture domain knowledge.